Search Results for author:
Found 97 papers, 47 papers with code
How Should I Build A Benchmark?
We propose How2Bench, which is comprised of a 55- 55-criteria checklist as a set of guidelines to govern the development of code-related benchmarks comprehensively.
LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance
Dataset license compliance is a critical yet complex aspect of developing commercial AI products, particularly with the increasing use of publicly available datasets.
How Well Do LLMs Generate Code for Different Application Domains? Benchmark and Evaluation
However, existing code generation benchmarks primarily focus on general-purpose scenarios, leaving the code generation performance of LLMs for specific application domains largely unknown.
RepoTransBench: A Real-World Benchmark for Repository-Level Code Translation
However, even with this improvement, the Success@1 score of the best-performing LLM is only 21%, which may not meet the need for reliable automatic repository-level code translation.
Mitigating Social Bias in Large Language Models: A Multi-Objective Approach within a Multi-Agent Framework
Our experiments conducted on two datasets and two models demonstrate that MOMA reduces bias scores by up to 87. 7%, with only a marginal performance degradation of up to 6. 8% in the BBQ dataset.
MemHunter: Automated and Verifiable Memorization Detection at Dataset-scale in LLMs
Large language models (LLMs) have been shown to memorize and reproduce content from their training data, raising significant privacy concerns, especially with web-scale datasets.
Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective
Graph autoencoders (GAEs) are self-supervised learning models that can learn meaningful representations of graph-structured data by reconstructing the input graph from a low-dimensional latent space.
GLA-DA: Global-Local Alignment Domain Adaptation for Multivariate Time Series
Unsupervised and Semi-supervised Domain Adaptation (UDA and SSDA) have demonstrated efficiency in addressing this issue by utilizing pre-labeled source data to train on unlabeled or partially labeled target data.
SCA: Highly Efficient Semantic-Consistent Unrestricted Adversarial Attack
Deep neural network based systems deployed in sensitive environments are vulnerable to adversarial attacks.
LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
In this paper, we conduct an empirical study to study the phenomena, mechanism, and mitigation of LLM hallucinations within more practical and complex development contexts in repository-level generation scenario.
A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning
In this work, we propose a non-isotropic sampling method to improve the gradient estimation procedure.
RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code
Despite their benefits, LLMs also pose notable risks, including the potential to generate harmful content and being abused by malicious developers to create malicious code.
Agents in Software Engineering: Survey, Landscape, and Vision
In recent years, Large Language Models (LLMs) have achieved remarkable success and have been widely used in various downstream tasks, especially in the tasks of the software engineering (SE) field.
L^2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering
Surprisingly, we find that L2CL, using only one-hop contrastive learning paradigm, is able to capture intrinsic semantic structures and improve the quality of node representation, leading to a simple yet effective architecture.
Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models
In this paper, we empirically analyze the differences in coding style between the code generated by mainstream Code LLMs and the code written by human developers, and summarize coding style inconsistency taxonomy.
A Survey on Failure Analysis and Fault Injection in AI Systems
Moreover, this survey contributes to the field by providing a framework for fault diagnosis, evaluating the state-of-the-art in FI, and identifying areas for improvement in FI techniques to enhance the resilience of AI systems.
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
Accordingly, we propose a graph fairness framework based on invariant learning, namely FairINV, which enables the training of fair GNNs to accommodate various sensitive attributes within a single training session.
CoSQA+: Enhancing Code Search Dataset with Matching Code
Existing code search datasets are problematic: either using unrealistic queries, or with mismatched codes, and typically using one-to-one query-code pairing, which fails to reflect the reality that a query might have multiple valid code matches.
State Space Models on Temporal Graphs: A First-Principles Study
Recently, state space models (SSMs), which are framed as discretized representations of an underlying continuous-time linear dynamical system, have garnered substantial attention and achieved breakthrough advancements in independent sequence modeling.
Advances in Robust Federated Learning: Heterogeneity Considerations
In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources.
Fair Graph Representation Learning via Sensitive Attribute Disentanglement
In particular, the objective of group fairness is to ensure that the decisions made by GNNs are independent of the sensitive attribute.
Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs
Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause configuration properties based on logs.
SGHormer: An Energy-Saving Graph Transformer Driven by Spikes
However, the costs behind outstanding performances of GTs are higher energy consumption and computational overhead.
Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions
We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration.
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning
FedBRB can uses small local models to train all blocks of the large global model, and broadcasts the trained parameters to the entire space for faster information interaction.
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
This implies that our FedCEO can effectively recover the disrupted semantic information by smoothing the global semantic space for different privacy settings and continuous training processes.
Training and Serving System of Foundation Models: A Comprehensive Survey
Additionally, the paper summarizes the challenges and presents a perspective on the future development direction of foundation model systems.
Improving Transferable Targeted Adversarial Attacks with Model Self-Enhancement
WS obtains an approximate ensemble of numerous pruned models to perform model augmentation which can be conveniently synergized with SASD to elevate the source model's generalization capability and thus improve the resultant targeted perturbations' transferability.
Rethinking and Simplifying Bootstrapped Graph Latents
Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing model collapse and producing distinguishable representations.
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation
Experiments on several benchmark datasets demonstrate that FairGKD, which does not require access to demographic information, significantly improves the fairness of GNNs by a large margin while maintaining their utility.
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.
VeryFL: A Verify Federated Learning Framework Embedded with Blockchain
These mechanism on blockchain shows an underlying support of blockchain for federated learning to provide a verifiable training, aggregation and incentive distribution procedure and thus we named this framework VeryFL (A Verify Federated Learninig Framework Embedded with Blockchain).
Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform
By using the model watermarking technology, we point out the possibility of building a unified platform for model ownership verification.
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training
However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance.
Contrastive Deep Nonnegative Matrix Factorization for Community Detection
Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability.
Ranked #1 on
Community Detection
on Pubmed
Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.
Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale
The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products.
SAILOR: Structural Augmentation Based Tail Node Representation Learning
In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes.
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion
To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples.
Contextual Dictionary Lookup for Knowledge Graph Completion
We extend several KGE models with the method, resulting in substantial performance improvements on widely-used benchmark datasets.
Migrate Demographic Group For Fair GNNs
To tackle this problem, we propose a brand new framework, FairMigration, which can dynamically migrate the demographic groups instead of keeping that fixed with raw sensitive attributes.
Oversmoothing: A Nightmare for Graph Contrastive Learning?
Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance.
Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond
Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI development.
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overlooked) for real-world applications.
Attention Paper: How Generative AI Reshapes Digital Shadow Industry?
The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning.
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
We approach the problem by our proposed STEP, a self-supervised temporal pruning framework that learns to remove potentially redundant edges from input dynamic graphs.
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning
Concretely, FSGCL first introduces a motif-based graph construction, which employs graph motifs to extract diverse semantics existed in graphs from the perspective of input data.
HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic Services
Besides, experiments with different training datasets, different optimization objectives and different machine learning platforms verify that HyperTuner can well adapt to various data analytic service scenarios.
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training
By lowering the volatility of the stock recommendation model, SVAT effectively reduces investment risks and outperforms state-of-the-art baselines by more than 30% in terms of risk-adjusted profits.
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph
DSLAD is a self-supervised method with anomaly discrimination and representation learning decoupled for anomaly detection.
Spectral Adversarial Training for Robust Graph Neural Network
In this work, we seek to address these challenges and propose Spectral Adversarial Training (SAT), a simple yet effective adversarial training approach for GNNs.
GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections
Specifically, to make the node injections as imperceptible and effective as possible, we propose a sampling operation to determine the degree of the newly injected nodes, and then generate features and select neighbors for these injected nodes based on the statistical information of features and evolutionary perturbations obtained from a genetic algorithm, respectively.
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e. g., Graph Neural Networks (GNNs).
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
We explore a new direction in that we can capture the evolving dynamics of temporal graphs with spiking neural networks (SNNs) instead of RNNs.
A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots
In addition, by integrating a once-through learning approach, the speed of semantics purification is accelerated while reducing the impact on the quality of replies.
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
A Survey of Deep Learning Models for Structural Code Understanding
In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights.
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation
Graph contrastive learning (GCL), as a popular approach to graph self-supervised learning, has recently achieved a non-negligible effect.
GUARD: Graph Universal Adversarial Defense
To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks.
Distributed Evolution Strategies for Black-box Stochastic Optimization
This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms.
HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems
Due to the complex interactions among multiple options and the high cost of performance measurement under a huge configuration space, it is challenging to study how different configurations influence the system performance.
MMES: Mixture Model based Evolution Strategy for Large-Scale Optimization
This work provides an efficient sampling method for the covariance matrix adaptation evolution strategy (CMA-ES) in large-scale settings.
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.
Neighboring Backdoor Attacks on Graph Convolutional Network
To address such a challenge, we set the trigger as a single node, and the backdoor is activated when the trigger node is connected to the target node.
Selective transfer learning with adversarial training for stock movement prediction
To validate our method, we perform the back-testing on the historical data of two public datasets and a newly constructed dataset.
Understanding Structural Vulnerability in Graph Convolutional Networks
In this work, we theoretically and empirically demonstrate that structural adversarial examples can be attributed to the non-robust aggregation scheme (i. e., the weighted mean) of GCNs.
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee
In addition, we theoretically analyze and prove the convergence of CMFL under different election and selection strategies, which coincides with the experimental results.
FedGL: Federated Graph Learning Framework with Global Self-Supervision
Moreover, the global self-supervision enables the information of each client to flow and share in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients.
GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data.
A_Blockchain-Based_Decentralized_Federated_Learning_Framework_with_Committee_Consensus
To address these security issues, we propose a decentralized federated learning framework based on blockchain, that is, a Blockchain- based Federated Learning framework with Committee consensus (BFLC).
Tensor Completion via Convolutional Sparse Coding Regularization
On the contrary, in the field of signal processing, Convolutional Sparse Coding (CSC) can provide a good representation of the high-frequency component of the image, which is generally associated with the detail component of the data.
MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks
To tackle the dilemma between accuracy and efficiency, we propose to use aggregators with different granularities to gather neighborhood information in different layers.
Ensemble Knowledge Distillation for CTR Prediction
Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications.
Adversarial Attack on Large Scale Graph
Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance.
Outlier-Resilient Web Service QoS Prediction
The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates.
A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus
To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i. e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC).
XBlock-EOS: Extracting and Exploring Blockchain Data From EOSIO
It is challenging to process and analyze a high volume of raw EOSIO data and establish the mapping from original raw data to the well-grained datasets since it requires substantial efforts in extracting various types of data as well as sophisticated knowledge on software engineering and data analytics.
Computational Engineering, Finance, and Science Cryptography and Security
Modelling High-Order Social Relations for Item Recommendation
The prevalence of online social network makes it compulsory to study how social relations affect user choice.
An Uncoupled Training Architecture for Large Graph Learning
Instead of directly processing the coupled nodes as GCNs, Node2Grids supports a more efficacious method in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the efficient Convolutional Neural Network (CNN).
A Survey of Adversarial Learning on Graphs
To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases
In the dynamic environment, the mobile devices selected by the existing reactive candidate-selection algorithms very possibly fail to complete the training and reporting phases of FL, because the FL parameter server only knows the currently-observed resources of all candidates.
PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks
Therefore, to provide the byzantine-resilience for distributed learning in 5G era, this article proposes a secure computing framework based on the sharding-technique of blockchain, namely PIRATE.
Distributed, Parallel, and Cluster Computing Cryptography and Security
Towards Better Forecasting by Fusing Near and Distant Future Visions
Multivariate time series forecasting is an important yet challenging problem in machine learning.
Blockchain Intelligence: When Blockchain Meets Artificial Intelligence
Blockchain is gaining extensive attention due to its provision of secure and decentralized resource sharing manner.
Data Poisoning Attacks on Neighborhood-based Recommender Systems
2) the fake users can be transferred to attack the state-of-the-art collaborative filtering recommender systems such as Neural Collaborative Filtering and Bayesian Personalized Ranking Matrix Factorization.
XBlock-ETH: Extracting and Exploring Blockchain Data From Ethereum
We name these well-processed Ethereum datasets as XBlock-ETH, which consists of the data of blockchain transactions, smart contracts, and cryptocurrencies (i. e., tokens).
Cryptography and Security
Selecting Reliable Blockchain Peers via Hybrid Blockchain Reliability Prediction
Blockchain and blockchain-based decentralized applications are attracting increasing attentions recently.
Software Engineering Distributed, Parallel, and Cluster Computing
Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression
Data compression is essential to reduce the cost of log storage.
Databases Software Engineering
Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities
We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA.
T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis
By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp.
Social and Information Networks Applications
Tools and Benchmarks for Automated Log Parsing
Logs are imperative in the development and maintenance process of many software systems.
Software Engineering
Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection
Considering the complicated and diversified topology structures of real-world networks, it is highly possible that the mapping between the original network and the community membership space contains rather complex hierarchical information, which cannot be interpreted by classic shallow NMF-based approaches.
Ranked #1 on
Node Classification
on Wiki
Collaborative Deep Learning Across Multiple Data Centers
Valuable training data is often owned by independent organizations and located in multiple data centers.
Learning Semantic Representations for Unsupervised Domain Adaptation
Prior domain adaptation methods address this problem through aligning the global distribution statistics between source domain and target domain, but a drawback of prior methods is that they ignore the semantic information contained in samples, e. g., features of backpacks in target domain might be mapped near features of cars in source domain.
Ranked #9 on
Domain Adaptation
on SVHN-to-MNIST
Learning Semantic Representations
Unsupervised Domain Adaptation
A Directed Acyclic Graph Approach to Online Log Parsing
A typical log-based system reliability management procedure is to first parse log messages because of their unstructured format; and apply data mining techniques on the parsed logs to obtain critical system behavior information.
Software Engineering
Detecting Ponzi Schemes on Ethereum: Towards Healthier Blockchain Technology
Blockchain technology becomes increasingly popular.
